Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2015

Abstract

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Keywords

Blogging, Data Mining, Marketing, Social Media, Support Vector Machine

Discipline

Computer Engineering | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

PLoS ONE

Volume

10

Issue

4

First Page

1

Last Page

20

ISSN

1932-6203

Identifier

10.1371/journal.pone.0122855

Publisher

Public Library of Science

Embargo Period

4-25-2021

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1371/journal.pone.0122855

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